code: def ssd_300_resnet34_v1b_coco(
      pretrained=False, pretrained_base=True, **kwargs
  ):
      """SSD architecture with ResNet v1b 34 layers.
  
      Parameters
      ----------
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      pretrained_base : bool or str, optional, default is True
          Load pretrained base network, the extra layers are randomized.
      norm_layer : object
          Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
          Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
      norm_kwargs : dict
          Additional `norm_layer` arguments, for example `num_devices=4`
          for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  
      Returns
      -------
      HybridBlock
          A SSD detection network.
      """
      from .resnet_v1b_ssd import resnet34_v1b_ssd
      from ...data import COCODetection
  
      classes = COCODetection.CLASSES
  
      return get_ssd(
          "resnet34_v1b",
          300,
          features=resnet34_v1b_ssd,
          filters=None,
          sizes=[21, 45, 99, 153, 207, 261, 315],
          ratios=[[1, 2, 0.5]]
          + [[1, 2, 0.5, 3, 1.0 / 3]] * 3
          + [[1, 2, 0.5]] * 2,
          steps=[8, 16, 32, 64, 100, 300],
          classes=classes,
          dataset="coco",
          pretrained=pretrained,
          pretrained_base=pretrained_base,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import warnings
  from gluoncv.data import VOCDetection
  from .ssd import get_ssd
  from ..ssd.anchor import LiteAnchorGenerator
  from ..ssd.vgg_atrous import vgg16_atrous_300, vgg16_atrous_512
label_desc: Use image node_id 23 for calling a global function with example usage: ssd_300_resnet34_v1b_coco(pretrained, pretrained_base) and returns: get_ssd